Machine learning refers to techniques for using computing systems to train predictive models that use past training examples to predict the outcome of future events that are similarly situated as the training examples. For example, machine learning can be used to train a predictive model, or for brevity, model, that predicts the market value of a house given particular attributes of the house, e.g., square footage, ZIP code, etc. The attributes are referred to as features of the model. A collection of features associated with a single data point used to train the model is referred to as a training example.
Many large enterprises and government institutions utilize machine learning to generate predictions of many different types of phenomena, e.g., oil demand for a particular region, the incidence rate of the flu virus in January, and the likelihood that a prospective borrower is likely to default on a mortgage.
Many of such organizations rely on users to manually enter information for completing a particular intended task. Such data entry is often cumbersome and inefficient. For example, user interfaces that use forms are tedious and often overly rigid. On the other hand, unstructured text fields give little or no guidance for what data is required for a particular task.